首页> 外文会议>Computational Intelligence for Multimedia Signal and Vision Processing, 2009. CIMSVP '09 >Image registration for sequence of visual images captured by UAV
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Image registration for sequence of visual images captured by UAV

机译:无人机配准的视觉图像序列的图像配准

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Unmanned aerial vehicles (UAVs) are regularly outfitted with payloads that include high resolution surveillance cameras. These surveillance systems have provided the military with the opportunity to monitor battlefields and remote terrain, carryout reconnaissance missions and track targets all from distant ground stations without endangering UAV operators. As with any remote sensing technology there are technical challenges that exist. The problem often arises where large sequences of images containing significant redundancies are being sent to the ground station processing systems and operators. Consequently, the ground station systems can become ldquoweighed downrdquo and operators can become overwhelmed. In addition, the decision making process of detection and recognition algorithms could be rendered ineffective because of the limited visual field of view of individual image frames. In this research image registration software is designed and implemented to integrate a sequence of visual images based on the modified scale-invariant feature transform (SIFT) algorithm. Since computational efficiency is a critical issue in any real-time interactive system, a modified version of the SIFT algorithm is devised and utilized in this work. Implementation and testing results of the developed software are obtained from real data collected from aerial footage and a collaborative camera network.
机译:无人机(UAV)定期配备有效载荷,其中包括高分辨率监控摄像头。这些监视系统为军方提供了监视战场和偏远地区,执行侦察任务并跟踪所有来自遥远地面站的目标的机会,而不会危及无人机操作员。与任何遥感技术一样,存在技术挑战。当包含重要冗余的大图像序列被发送到地面站处理系统和操作员时,通常会出现问题。因此,地面站系统可能会陷入困境,运营商可能会不知所措。另外,由于单个图像帧的有限视野,检测和识别算法的决策过程可能变得无效。在这项研究中,图像配准软件的设计和实现是基于改进的尺度不变特征变换(SIFT)算法来集成视觉图像序列。由于计算效率是任何实时交互式系统中的关键问题,因此,本文设计并利用了SIFT算法的修改版本。开发的软件的实施和测试结果是从航拍镜头和协作摄像机网络收集的真实数据中获得的。

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